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r-rgeolocate
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Connectors to online and offline sources for taking IP addresses and geolocating them to country, city, timezone and other geographic ranges. For individual connectors, see the package index.
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2023-06-18 |
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r-mcglm
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Fitting multivariate covariance generalized linear models (McGLMs) to data. McGLM is a general framework for non-normal multivariate data analysis, designed to handle multivariate response variables, along with a wide range of temporal and spatial correlation structures defined in terms of a covariance link function combined with a matrix linear predictor involving known matrices. The models take non-normality into account in the conventional way by means of a variance function, and the mean structure is modelled by means of a link function and a linear predictor. The models are fitted using an efficient Newton scoring algorithm based on quasi-likelihood and Pearson estimating functions, using only second-moment assumptions. This provides a unified approach to a wide variety of different types of response variables and covariance structures, including multivariate extensions of repeated measures, time series, longitudinal, spatial and spatio-temporal structures. The package offers a user-friendly interface for fitting McGLMs similar to the glm() R function. See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information and examples.
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2023-06-18 |
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r-maxact
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Perform exact MAX3 or MAX2 test for one-locus genetic association analysis and trend test for dominant, recessive and additive models. It can also calculate approximated p-value with the normal approximation method.
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2023-06-18 |
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r-matmanlymix
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Matrix clustering with finite mixture models.
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2023-06-18 |
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r-lxb
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Functions to quickly read LXB parameter data.
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2023-06-18 |
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r-lvec
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Core functionality for working with vectors (numeric, integer, logical and character) that are too large to keep in memory. The vectors are kept (partially) on disk using memory mapping. This package contains the basic functionality for working with these memory mapped vectors (e.g. creating, indexing, ordering and sorting) and provides C++ headers which can be used by other packages to extend the functionality provided in this package.
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2023-06-18 |
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r-lpstimeseries
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Learned Pattern Similarity (LPS) for time series. Implements a novel approach to model the dependency structure in time series that generalizes the concept of autoregression to local auto-patterns. Generates a pattern-based representation of time series along with a similarity measure called Learned Pattern Similarity (LPS). Introduces a generalized autoregressive kernel.This package is based on the 'randomForest' package by Andy Liaw.
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2023-06-18 |
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r-lmenbbayes
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The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
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2023-06-18 |
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r-libsoc
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Handle 'PharmML' (Pharmacometrics Markup Language) standard output (SO) XML files. SO files can be created, read, manipulated and written through a data binding from the XML structure to a tree structure of R objects.
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2023-06-18 |
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r-liblinear.acf
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Solving the linear SVM problem with coordinate descent is very efficient and is implemented in one of the most often used packages, 'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear). It has been shown that the uniform selection of coordinates can be accelerated by using an online adaptation of coordinate frequencies (ACF). This package implements ACF and is based on 'LIBLINEAR' as well as the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>). It currently supports L2-regularized L1-loss as well as L2-loss linear SVM. Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and Crammer & Singer method) and cross validation for model selection is supported. The training of the models based on ACF is much faster than standard 'LIBLINEAR' on many problems.
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2023-06-18 |
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r-lclgwas
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The core of this 'Rcpp' based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the 'Cuhre' algorithm from the 'Cuba' library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the 'Cuhre' function are included in this package. The maximization process is carried out using Brent's algorithm, with the 'C++' code file from John Burkardt and John Denker (Brent, R.,Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).
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2023-06-18 |
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r-icapca
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Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources
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2023-06-18 |
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r-htree
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Historical regression trees are an extension of standard trees, producing a non-parametric estimate of how the response depends on all of its prior realizations as well as that of any time-varying predictor variables. The method applies equally to regularly as well as irregularly sampled data. The package implements random forest and boosting ensembles based on historical regression trees, suitable for longitudinal data. Standard error estimation and Z-score variable importance is also implemented.
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2023-06-18 |
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r-htmltidy
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HTML documents can be beautiful and pristine. They can also be wretched, evil, malformed demon-spawn. Now, you can tidy up that HTML and XHTML before processing it with your favorite angle-bracket crunching tools, going beyond the limited tidying that 'libxml2' affords in the 'XML' and 'xml2' packages and taming even the ugliest HTML code generated by the likes of Google Docs and Microsoft Word. It's also possible to use the functions provided to format or "pretty print" HTML content as it is being tidied. Utilities are also included that make it possible to view formatted and "pretty printed" HTML/XML content from HTML/XML document objects, nodes, node sets and plain character HTML/XML using 'vkbeautify' (by Vadim Kiryukhin) and 'highlight.js' (by Ivan Sagalaev). Also (optionally) enables filtering of nodes via XPath or viewing an HTML/XML document in "tree" view using 'XMLDisplay' (by Lev Muchnik). See <https://github.com/vkiryukhin/vkBeautify> and <http://www.levmuchnik.net/Content/ProgrammingTips/WEB/XMLDisplay/DisplayXMLFileWithJavascript.html> for more information about 'vkbeautify' and 'XMLDisplay', respectively.
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2023-06-18 |
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r-hier.part
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Partitioning of the independent and joint contributions of each variable in a multivariate data set, to a linear regression by hierarchical decomposition of goodness-of-fit measures of regressions using all subsets of predictors in the data set. (i.e., model (1), (2), ..., (N), (1,2), ..., (1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of each predictor is provided by using a randomisation test.
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2023-06-18 |
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r-hi
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Simulation from distributions supported by nested hyperplanes, using the algorithm described in Petris & Tardella, "A geometric approach to transdimensional Markov chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4, (2003). Also random direction multivariate Adaptive Rejection Metropolis Sampling.
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2023-06-18 |
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r-hdlm
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Mimics the lm() function found in the package stats to fit high dimensional regression models with point estimates, standard errors, and p-values. Methods for printing and summarizing the results are given.
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2023-06-18 |
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r-gwfa
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Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country.
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2023-06-18 |
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r-groupsubsetselection
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Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.
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2023-06-18 |
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r-groupremmap
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An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.
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2023-06-18 |
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r-gpclib
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General polygon clipping routines for R based on Alan Murta's C library.
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2023-06-18 |
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r-gmeta
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An implementation of an all-in-one function for a wide range of meta-analysis problems. It contains three functions. The gmeta() function unifies all standard meta-analysis methods and also several newly developed ones under a framework of combining confidence distributions (CDs). Specifically, the package can perform classical p-value combination methods (such as methods of Fisher, Stouffer, Tippett, etc.), fit meta-analysis fixed-effect and random-effects models, and synthesizes 2x2 tables. Furthermore, it can perform robust meta-analysis, which provides protection against model-misspecifications, and limits the impact of any unknown outlying studies. In addition, the package implements two exact meta-analysis methods from synthesizing 2x2 tables with rare events (e.g., zero total event). The np.gmeta() function summarizes information obtained from multiple studies and makes inference for study-level parameters with no distributional assumption. Specifically, it can construct confidence intervals for unknown, fixed study-level parameters via confidence distribution. Furthermore, it can perform estimation via asymptotic confidence distribution whether tie or near tie condition exist or not. The plot.gmeta() function to visualize individual and combined CDs through extended forest plots is also available. Compared to version 2.2-6, version 2.3-0 contains a new function np.gmeta().
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2023-06-18 |
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r-keyboardsimulator
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public |
Control your keyboard and mouse with R code by simulating key presses and mouse clicks. The input simulation is implemented with the Windows API.
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2023-06-16 |
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r-lasso2
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Routines and documentation for solving regression problems while imposing an L1 constraint on the estimates, based on the algorithm of Osborne et al. (1998).
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2023-06-16 |
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r-largelist
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Functions to write or append a R list to a file, as well as read, remove, modify elements from it without restoring the whole list.
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2023-06-16 |
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r-kolmim
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Provides an alternative, more efficient evaluation of extreme probabilities of Kolmogorov's goodness-of-fit measure, Dn, when compared to the original implementation of Wang, Marsaglia, and Tsang. These probabilities are used in Kolmogorov-Smirnov tests when comparing two samples.
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2023-06-16 |
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r-knor
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The k-means 'NUMA' Optimized Routine library or 'knor' is a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access ('NUMA') architectures.
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2023-06-16 |
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r-knncat
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Scale categorical variables in such a way as to make NN classification as accurate as possible. The code also handles continuous variables and prior probabilities, and does intelligent variable selection and estimation of both error rates and the right number of NN's.
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2023-06-16 |
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r-kfksds
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Naive implementation of the Kalman filter, smoother and disturbance smoother for state space models.
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2023-06-16 |
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r-kexpmv
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Implements functions from 'EXPOKIT' (<https://www.maths.uq.edu.au/expokit/>) to calculate matrix exponentials, Sidje RB, (1998) <doi:10.1145/285861.285868>. Includes functions for small dense matrices along with functions for large sparse matrices. The functions for large sparse matrices implement Krylov subspace methods which help minimise the computational complexity for matrix exponentials. 'Kexpmv' can be utilised to calculate both the matrix exponential in isolation along with the product of the matrix exponential and a vector.
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2023-06-16 |
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r-kaps
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This package provides some routines to conduct the K-adaptive parititioning (kaps) algorithm for survival data. A function kaps is an implementation version of our algorithm.
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2023-06-16 |
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r-jwutil
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This is a set of simple utilities for various data manipulation and testing tasks. The goal is to use core R tools well, without bringing in many dependencies. Main areas of interest are semi-automated data frame manipulation, such as converting factors in multiple binary indicator columns. There are testing functions which provide 'testthat' expectations to permute arguments to function calls. There are functions and data to test extreme numbers, dates, and bad input of various kinds which should allow testing failure and corner cases, which can be used for fuzzing your functions. The test suite has many examples of usage.
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2023-06-16 |
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r-jumptest
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A fast simulation on stochastic volatility model, with jump tests, p-values pooling, and FDR adjustments.
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2023-06-16 |
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r-jtgwas
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The core of this 'Rcpp' based package is a function to compute standardized Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, e.g., genome-wide analysis. It implements 'OpenMP', allowing the option of computing on multiple threads. Supporting functions are also provided to calculate p-values and summarize results.
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2023-06-16 |
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r-jrf
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Simultaneous estimation of multiple related networks.
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2023-06-16 |
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r-jmdl
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Fit joint mean-correlation models for discrete longitudinal data (Tang CY,Zhang W, Leng C, 2017 <doi:10.5705/ss.202016.0435>).
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2023-06-16 |
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r-jaspar
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R modules for JASPAR data processing and visualization
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2023-06-16 |
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r-jaguar
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Implements a novel score test that measures 1) the overall shift in the gene expression due to genotype (additive genetic effect), and 2) group-specific changes in gene expression due to genotype (interaction effect) in a mixed-effects model framework.
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2023-06-16 |
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r-itree
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This package is based on the code of the rpart package. It extends rpart by adding additional splitting methods emphasizing interpretable/parsimonious trees. Unless indicated otherwise, it is safe to assume that all functions herein are extensions of or copied directly from similar or nearly identical rpart methods. As such, the authors of rpart are authors of this package as well. However, please direct any error reports or other questions about itree to the maintainer of this package; they are welcome and appreciated.
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2023-06-16 |
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r-itrlearn
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Maximin-projection learning (MPL, Shi, et al., 2018) is implemented for recommending a meaningful and reliable individualized treatment regime for future groups of patients based on the observed data from different populations with heterogeneity in individualized decision making. Q-learning and A-learning are implemented for estimating the groupwise contrast function that shares the same marginal treatment effects. The packages contains classical Q-learning and A-learning algorithms for a single stage study as a byproduct. More functions will be added at later versions.
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2023-06-16 |
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r-isr3
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Performs multivariate normal imputation through iterative sequential regression. Conditional dependency structure between imputed variables can be specified a priori to accelerate imputation.
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2023-06-16 |
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r-isqg
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Accomplish high performance simulations in quantitative genetics. The molecular genetic components are represented by R6/C++ classes and methods. The core computational algorithm is implemented using bitsets according to <doi:10.1534/g3.119.400373>. A mix between low and high level interfaces provides great flexibility and allows user defined extensions and a wide range of applications.
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2023-06-16 |
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r-isbf
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Selection of features for sparse regression estimation (like the LASSO). Selection of blocks of features when the regression parameter is sparse and constant by blocks (like the Fused-LASSO). Application to cgh arrays.
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2023-06-16 |
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r-irregular1
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Simulation and density evaluation of irregularly sampled stationary AR(1) processes with Gaussian errors using the algorithms described in Allévius (2018) <arXiv:1801.03791>.
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2023-06-16 |
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r-interact
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This package searches for marginal interactions in a binary response model. Interact uses permutation methods to estimate false discovery rates for these marginal interactions and has some, limited visualization capabilities
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2023-06-16 |
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r-independencetests
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Functions for testing mutual independence between many numerical random vectors or serial independence of a multivariate stationary sequence. The proposed test works when some or all of the marginal distributions are singular with respect to Lebesgue measure.
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2023-06-16 |
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r-inarmix
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Fits mixtures models for longitudinal data. Appropriate when the data are counts and when the correlation structure is assumed to be AR(1).
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2023-06-16 |
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r-imputemdr
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This package provides various approaches to handling missing values for the MDR analysis to identify gene-gene interactions using biallelic marker data in genetic association studies
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2023-06-16 |
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r-image
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Performs mQTL (methylation quantitative-trait locus) mapping in bisulfite sequencing studies by fitting a binomial mixed model and then incorporating the allelic-specific methylation pattern. Based on Fan, Yue; Vilgalys, Tauras P.; Sun, Shiquan; Peng, Qinke; Tung, Jenny; Zhou, Xiang (2019) <doi:10.1101/615039>.
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2023-06-16 |
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r-im
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Compute moments of images and perform reconstruction from moments.
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2023-06-16 |
